Classification of social media Toxic comments using Machine learning models

• URL: http://arxiv.org/abs/2304.06934v1
• Date: Fri, 14 Apr 2023 05:40:11 GMT
• Title: Classification of social media Toxic comments using Machine learning models
• Authors: K.Poojitha, A.Sai Charish, M.Arun Kuamr Reddy, S.Ayyasamy
• Abstract summary: The abstract outlines the problem of toxic comments on social media platforms, where individuals use disrespectful, abusive, and unreasonable language. This behavior is referred to as anti-social behavior, which occurs during online debates, comments, and fights. The comments containing explicit language can be classified into various categories, such as toxic, severe toxic, obscene, threat, insult, and identity hate. To protect users from offensive language, companies have started flagging comments and blocking users.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The abstract outlines the problem of toxic comments on social media platforms, where individuals use disrespectful, abusive, and unreasonable language that can drive users away from discussions. This behavior is referred to as anti-social behavior, which occurs during online debates, comments, and fights. The comments containing explicit language can be classified into various categories, such as toxic, severe toxic, obscene, threat, insult, and identity hate. This behavior leads to online harassment and cyberbullying, which forces individuals to stop expressing their opinions and ideas. To protect users from offensive language, companies have started flagging comments and blocking users. The abstract proposes to create a classifier using an Lstm-cnn model that can differentiate between toxic and non-toxic comments with high accuracy. The classifier can help organizations examine the toxicity of the comment section better.

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